Systematization of Pseudonymization Algorithms for Health Data

Abstract

Biomedical research depends on collecting, analyzing, and sharing of data while protecting patient or study participant privacy. Laws such as the European Union’s General Data Protection Regulation recommend pseudonymization, which provides a mechanism to protect privacy by replacing personally identifiable information with pseudonyms. We present a comparative analysis of pseudonymization algorithms categorized into encryption-, hash-, counter-, and randomness-based methods, structured along eight key axes such as pseudonym length, complexity, and suitability for automation or manual transmission. This analysis highlights the strengths and limitations of each method, assisting researchers in making informed decisions about pseudonymization for health data.